Purpose: to provide an update on the management of a Urology Department during the COVID-19 outbreak, suggesting strategies to optimize assistance to the patients, to implement telemedicine and triage protocols, to define pathways for hospital access, to reduce risk of contagious inside the hospital and to determine the role of residents during the pandemic. Materials and Methods: In May the 6 th 2020 we performed a review of the literature through online search engines (PubMed, Web of Science and Science Direct). We looked at recommendations provided by the EAU and ERUS regarding the management of urological patients during the COVID-19 pandemic. The main aspects of interest were: the definition of deferrable and non-deferrable procedures, Personal Protective Equipment (PPE) and hospital protocols for health care providers, triage, hospitalization and surgery, post-operative care training and residents' activity. A narrative summary of guidelines and current literature for each point of interest was performed. Conclusion: In the actual Covid-19 scenario, while the number of positive patients globally keep on rising, it is fundamental to embrace a new way to deliver healthcare and to overcome challenges of physical distancing and self-isolation. The use of appropriate PPE, definite pathways to access the hospital, the implementation of telemedicine protocols can represent effective strategies to carry on delivering healthcare.

Purpose of review We aim to summarize the current state of art about 3D applications in urology focusing on kidney surgeries. In addition we aim to provide a snapshot about future perspective of intraoperative applications of augmented reality (AR). Recent findings A variety of applications in different fields have been proposed. Many applications concern current realities and 3D reconstruction, while some others are about future perspective. The majority of recent studies have focused their attention on preoperative surgical planning, patient education, surgical training, and AR. Summary The disposability of 3D models in healthcare scenarios might improve surgical outcomes, learning curves of novice surgeons and residents, as well as patients’ understanding and compliance, allowing a more shared surgical decision-making.

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Background: To optimize results reporting after penile cancer (PC) surgery, we proposed a Tetrafecta and assessed its ability to predict overall survival (OS) probabilities. Methods: A purpose-built multicenter, multi-national database was queried for stage I–IIIB PC, requiring inguinal lymphadenectomy (ILND), from 2015 onwards. Kaplan–Meier (KM) method assessed differences in OS between patients achieving Tetrafecta or not. Univariable and multivariable regression analyses identified its predictors. Results: A total of 154 patients were included in the analysis. The 45 patients (29%) that achieved the Tetrafecta were younger (59 vs. 62 years; p = 0.01) and presented with fewer comorbidities (ASA score ≥ 3: 0% vs. 24%; p < 0.001). Although indicated, ILND was omitted in 8 cases (5%), while in 16, a modified template was properly used. Although median LNs yield was 17 (IQR: 11–27), 35% of the patients had <7 nodes retrieved from the groin. At Kaplan–Maier analysis, the Tetrafecta cohort displayed significantly higher OS probabilities (Log Rank = 0.01). Uni- and multivariable logistic regression analyses identified age as the only independent predictor of Tetrafecta achievement (OR: 0.97; 95%CI: 0.94–0.99; p = 0.04). Conclusions: Our Tetrafecta is the first combined outcome to comprehensively report results after PC surgery. It is widely applicable, based on standardized and reproducible variables and it predicts all-cause mortality.

Background: A correct approach to recurrent urinary tract infections (rUTIs) is an important pillar of antimicrobial stewardship. We aim to define an Artificial Neural Network (ANN) for predicting the clinical efficacy of the empiric antimicrobial treatment in women with rUTIs. Methods: We extracted clinical and microbiological data from 1043 women. We trained an ANN on 725 patients and validated it on 318. Results: The ANN showed a sensitivity of 87.8% and specificity of 97.3% in predicting the clinical efficacy of empirical therapy. The previous use of fluoroquinolones (HR = 4.23; p = 0.008) and cephalosporins (HR = 2.81; p = 0.003) as well as the presence of Escherichia coli with resistance against cotrimoxazole (HR = 3.54; p = 0.001) have been identified as the most important variables affecting the ANN output decision predicting the fluoroquinolones-based therapy failure. A previous isolation of Escherichia coli with resistance against fosfomycin (HR = 2.67; p = 0.001) and amoxicillin-clavulanic acid (HR = 1.94; p = 0.001) seems to be the most influential variable affecting the output decision predicting the cephalosporins- and cotrimoxazole-based therapy failure. The previously mentioned Escherichia coli with resistance against cotrimoxazole (HR = 2.35; p < 0.001) and amoxicillin-clavulanic acid (HR = 3.41; p = 0.007) seems to be the most influential variable affecting the output decision predicting the fosfomycin-based therapy failure. Conclusions: ANNs seem to be an interesting tool to guide the antimicrobial choice in the management of rUTIs at the point of care.

Background: The aim of our study was to develop a radiomic tool for the prediction of clinically significant prostate cancer. Methods: From September 2020 to December 2021, 91 patients who underwent magnetic resonance imaging prostate fusion biopsy at our institution were selected. Prostate cancer aggressiveness was assessed by combining the three orthogonal planes-Llocal binary pattern the 3Dgray level co-occurrence matrix, and other first order statistical features with clinical (semantic) features. The 487 features were used to predict whether the Gleason score was clinically significant (≥7) in the final pathology. A feature selection algorithm was used to determine the most predictive features, and at the end of the process, nine features were chosen through a 10-fold cross validation. Results: The feature analysis revealed a detection accuracy of 83.5%, with a clinically significant precision of 84.4% and a clinically significant sensitivity of 91.5%. The resulting area under the curve was 80.4%. Conclusions: Radiomic analysis allowed us to develop a tool that was able to predict a Gleason score of ≥7. This new tool may improve the detection rate of clinically significant prostate cancer and overcome the limitations of the subjective interpretation of magnetic resonance imaging, reducing the number of useless biopsies.

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